Abstract:

A method for characterizing website visitors based on visitor passive
parameters and using the characterization to select and/or market website
content. The passive visitor parameters include data in the browser
agent, time of a website visit, IP address, etc. Such visitor passive
parameters are available each time a visitor visits a website. In a first
step, a first embodiment of the method anonymously compares the visitor
passive parameters with known demographics, for example, at financial
websites, to create a statistical mapping between the visitor passive
parameters and the demographics. In a second step, the mapping is used to
estimate demographics data for future website visitors and then site
content provided to the future website visitors is based on the estimated
demographics.

Claims:

1. A method for selecting website content, the method comprising:obtaining
a multiplicity of anonymous first website visitor passive parameters from
a first website for a multiplicity of first website visitors visiting the
first website, wherein the anonymous first website visitor passive
parameters are commonly available whenever a website is visited;obtaining
corresponding first website visitor active parameters of the first
website visitors from the first website, wherein the first website
visitor active parameters are correlatable to a value of website content
presentable to future website visitors;generating anonymous statistical
mappings between the first website visitor passive parameters of the
first website visitors and the corresponding first website visitor active
parameters of the first website visitors, wherein the statistical
mappings do not rely on any data identifying an individual website
visitor;providing the statistical mappings to at least one of website
operators, website publishers, search engine operators and Internet
advertisers;using the statistical mappings to establish an expected value
of future website content provided to the future website visitors, based
on future website visitor passive parameters of each of the future
website visitors; andproviding future website content to the future
website visitors based on the expected value of the future website
content.

2. The method of claim 1, wherein the first website visitor passive
parameters are selected from data in a browser agent, time of day when
visiting websites, and IP address for the first website visitors.

3. The method of claim 2, wherein the first website is a financial website
and the first website visitor active parameters are demographics
parameters.

4. The method of claim 2, wherein the first website is an e-commerce
website and the first website visitor active parameters are transaction
data.

5. The method of claim 1, wherein generating anonymous statistical
mappings comprises using direct mapping including statistical techniques
to estimate the likelihood that the website visitor has a specific
characteristic to generate the anonymous statistical mappings.

6. The method of claim 1, wherein generating anonymous statistical
mappings comprises using direct mapping including regression to estimate
the likelihood that the website visitor has a specific characteristic to
generate the anonymous statistical mappings.

7. The method of claim 1, wherein generating anonymous statistical
mappings comprises using Multi-Factor Mapping to estimate the likelihood
that the website visitor has a specific characteristic to generate the
anonymous statistical mappings.

8. A method for marketing Internet advertising, the method
comprising:obtaining a multiplicity of anonymous first website visitor
passive parameters from a financial website for a multiplicity of
financial website visitors visiting the financial website, wherein the
anonymous first website visitor passive parameters are commonly available
whenever a website is visited;obtaining corresponding financial website
visitor demographics of the financial website visitors, wherein a value
of website content presentable to future website visitors is correlatable
to the financial website visitor demographics;anonymously generating
statistical mappings between the first website visitor passive parameters
and the financial website visitor demographics of the financial website
visitors, wherein the statistical mappings do not rely on any data
identifying an individual website visitor;marketing the statistical
mappings to at least one of website publishers, Internet advertisers, and
second website operators; andusing the statistical mappings to perform at
least one of:identifying future website visitors having demographics
desirable to the website publishers and Internet advertisers;
anddemonstrating demographics of the second website visitors visiting
second websites operated by the second website operators.

9. A method for marketing Internet advertising, the method
comprising:obtaining anonymous first website visitor passive parameters
from a e-commerce website for a multiplicity of e-commerce website
visitors visiting the e-commerce website, wherein the anonymous first
website visitor passive parameters are commonly available whenever a
website is visited;obtaining transaction data from the e-commerce website
corresponding to the first website visitor passive parameters for the
e-commerce website visitors;generating statistical mappings from the
first website visitor passive parameters to the transaction data, wherein
the statistical mappings do not rely on any data identifying an
individual website visitor;marketing the statistical mappings to website
publishers and Internet advertisers;estimating expected values of
Internet advertising to future website visitors based on future website
visitor passive parameters of the future website visitors and the
statistical mappings; andallocating the Internet advertising to the
future website visitors based on the expected values of the advertising.

10. The method for marketing Internet advertising of claim 9, further
comprising:collecting additional transaction data for the Internet
advertising directed to the future website visitors; andgenerating
improved statistical mappings based on the additional transaction data.

Description:

BACKGROUND OF THE INVENTION

[0001]The present invention relates in general to online Internet
advertising optimization, and more specifically to analysis of
relationships between web browser agent data and characteristics and
behavior of website visitors.

[0002]There are many advertising systems and methods which are used to
select advertisements for display on Internet websites. These advertising
systems use various strategies and logic to select which products and/or
services may be of interest to an individual website visitor and how
advertisements for the selected products should appear within a website.
There are many competing ideas and many different approaches to designing
the logic which is used to select and display the advertising.

[0003]One such strategy is based on data which is collected for each
website visitor. In this strategy, a unique identifier (commonly, an
Internet browser cookie) is downloaded from the website to the visitors
computer. This unique identifier allows the advertising system to tag the
visitor and recognize the visitor's visits to the website as a discrete
individual. Further, data observed during the visit to the website is
collected and stored in a data base and indexed to the unique identifier.
This allows the advertising network to cross reference the stored data
about the visitor in the database each time the visitors computer
requests a web page. Observing a visitor's habits allows the advertising
network to better determine which ads to display based on the stored
data. A website may also retrieve visitor preferences and interests
stored at the website by identifying a returning visitor. The different
kinds of data which may be gathered and the means of referencing the data
based on the users identifier are important aspects of the strategy.

[0004]One common way to gather visitor interests is to observe the
visitor's path through the website and noting the topics of the pages
which the visitor views. Another way to gather data is to request that
the visitor fill out a survey and then store the survey information for
future use by the advertising system when the visitor returns. A third
common way to gather this data is by saving information supplied by the
visitor when purchasing goods. Oftentimes, the billing address given at
the conclusion of an e-commerce transaction can be used to purchase
demographic data from companies which compile such information on a wide
basis. Thus, there are several existing approaches to collecting and
referencing data for online advertising systems.

[0005]There are several concerns and problems with known methods of data
collection and indexing. One overarching issue is that the visitor's
privacy is threatened by the combined data gathering. Another potential
issue is that the cookie used to store the visitor's unique identifier
resides on the visitor's computer system. Visitors often delete these
cookies and thereby defeat the ability to recognize repeat visits. In
addition, due to privacy concerns, a market has developed for software
applications which remove cookies placed by advertising systems. The
result of removing the unique identifying cookie is that the advertising
network can no longer reference information stored in the database for
that visitor, and may incorrectly identify future visits by the same
visitor as an additional visitor.

BRIEF SUMMARY OF THE INVENTION

[0006]The present invention addresses the above and other needs by
providing a method for characterizing website visitors based on visitor
passive parameters and using the characterization to select and/or market
website content. The passive visitor parameters include data in the
browser agent, time of a website visit, IP address, etc. Such visitor
passive parameters are available each time a visitor visits a website. In
a first step, a first embodiment of the method anonymously compares the
visitor passive parameters with known demographics, for example, at
financial websites, to create a statistical mapping between the visitor
passive parameters and the demographics. In a second step, the mapping is
used to estimate demographics data for future website visitors and then
site content provided to the future website visitors is based on the
estimated demographics.

[0007]The present invention further uses browser agent data to predict the
characteristics of website visitors. First, data about the
characteristics of website visitors (including, but not limited to,
demographics information as well as specific behavioral information) is
matched statistically to user agent strings. Then, the user agent strings
are used to predict the characteristics of future visitors to a website.
The use of the present user agent data for these purposes facilitates the
other unique aspects of the invention described in the following.

[0008]The combination of the browser agent string and statistical analysis
of the present invention has several unique benefits not present in known
methods of predicting user characteristics or behavior. These benefits
are:

[0009](1) The browser agent string is always present, unlike browser
cookies which may be removed and cleared;

[0010](2) Emerging internet platforms and devices (e.g. cellular phones,
video game consoles) do not have the capability to store browser cookies
at all, while browser agents are still enabled and used;

[0011](3) Similarly, certain applications which are becoming increasingly
common, such as videos and Adobe Flash, do not support existing website
visitor tracking technologies, but could be analyzed using methods
according to the present invention;

[0012](4) The methods according to the present invention completely
protect website visitor privacy because individual website visitors are
never identified during the statistical mapping phase, and the browser
agents do not identify the website visitors; and

[0013](5) Because the browser agent is stored in log files of existing web
servers, the data may be post-analyzed without modifying the technical
infrastructure of the website.

[0014](6) The methods of applying statistical techniques to other
websites' passive and active parameters provides superior insight in
characterizing web visitors not available with current methods or passive
parameters alone.

[0015]In accordance with one aspect of the invention, there is provided a
first method for characterizing web site visitors so that online
advertising can be adjusted and optimized. The first method comprises
obtaining a multiplicity of anonymous first website visitor passive
parameters from a first website for a multiplicity of first website
visitors visiting the first website. Corresponding active parameters of
the first website visitors are also obtained from the first website.
Anonymous statistical mappings are generated between the first website
visitor passive parameters and the corresponding first website visitor
active parameters. The statistical mappings are provided to website
operators, website publishers, search engine operators, and/or Internet
advertisers. The website operators, website publishers, and/or Internet
advertisers use the statistical mappings to improve the expected value of
future website content provided to the future website visitors, based on
future passive website visitor parameters of each of the future website
visitors. Future website content is then provided to the future website
visitors based on the expected value of the future website content. The
first passive website visitor parameters are commonly available whenever
a website is visited and do not identify the website visitor and the
statistical mappings do not rely on any data identifying an individual
website visitor. The second website visitor parameters are correlatable
to a value of website content presentable to the future website visitors.

[0016]In accordance with another aspect of the invention, there is
provided a method for marketing Internet advertising. The method for
marketing Internet advertising includes obtaining a multiplicity of
anonymous first website visitor passive parameters from a financial
website for a multiplicity of financial website visitors visiting the
financial website. Corresponding financial website visitor demographics
of the financial website visitors are obtained, where a value of website
content presentable to future website visitors is correlatable to the
website visitor demographics. Statistical mappings are anonymously
generated between the first passive website visitor parameters and the
financial website visitor demographics of the financial website visitors,
where the statistical mappings do not rely on any data identifying an
individual website visitor. The statistical mappings are marketed to
website publishers, Internet advertisers, and/or second website
operators. The statistical mappings are used to perform at least one of
identifying future website visitors having demographics desirable to the
website publishers and Internet advertisers and demonstrating
demographics of the second website visitors visiting second websites
operated by the second website operators.

[0017]In accordance with yet another aspect of the invention, there is
provided a second method for marketing Internet advertising. The second
method for marketing Internet advertising includes obtaining anonymous
first website visitor passive parameters from an e-commerce website for a
multiplicity of e-commerce website visitors visiting the e-commerce
website, where the anonymous first passive website visitor parameters are
commonly available whenever a website is visited. Transaction data
corresponding to the first passive website visitor passive parameters for
the e-commerce website visitors is also obtained from the e-commerce
website. Statistical mappings are generated from the first website
visitor passive parameters to the transaction data, where the statistical
mappings do not rely on any data identifying an individual website
visitor. The statistical mappings are marketed to website publishers and
Internet advertisers and expected values of Internet advertising to
future website visitors based are estimated on future website visitor
passive parameters of the future website visitors and the statistical
mappings. The Internet advertising is then allocated to the future
website visitors based on the expected values of the advertising. The
second method for marketing Internet advertising may further include
collecting additional transaction data for the Internet advertising
directed to the future website visitors and generating improved
statistical mappings based on the additional transaction data.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING

[0018]The above and other aspects, features and advantages of the present
invention will be more apparent from the following more particular
description thereof, presented in conjunction with the following drawings
wherein:

[0019]FIG. 1 is a diagram of generation and application of a mapping
between passive website visitor parameters and demographics according to
the present invention.

[0020]FIG. 2 is a diagram of a second embodiment of generation and
application of a mapping between passive website visitor parameters and
demographics according to the present invention.

[0021]FIG. 3 describes a first method according to the present invention.

[0022]FIG. 4 describes a second method according to the present invention

[0023]FIG. 5 describes a third method according to the present invention.

[0024]FIG. 6 describes optional additional steps in the third method
according to the present invention.

[0025]Corresponding reference characters indicate corresponding components
throughout the several views of the drawings.

DETAILED DESCRIPTION OF THE INVENTION

[0026]The following description is of the best mode presently contemplated
for carrying out the invention. This description is not to be taken in a
limiting sense, but is made merely for the purpose of describing one or
more preferred embodiments of the invention. The scope of the invention
should be determined with reference to the claims.

[0027]A general diagram of a method for improving targeting of Internet
advertising according to the present invention is shown in FIG. 1. The
diagram includes a first phase for generating mappings 20 between website
visitor passive parameters and demographics, and a second phase of
applying the mappings 20 to individual website visitor passive parameters
to obtain demographics and basing Internet advertising on the
demographics. In general, parameters available whenever a website is
visited are referred to herein as passive parameters and are described in
more detail below. The passive parameters are anonymous and do not
identify an individual website visitor. The passive visitor parameters
may include data in the browser agent, time of day when visiting
websites, IP address, etc. for the website visitors 10a. A browser agent
is a set of parameters sent to a website by a website visitor visiting
the website to help the website determine how to format content to send
to the visitor. Such passive parameters are generally passively available
when any visitor merely visits a website, that is, available without
requesting information from the visitor and without attempting to
interrogate the visitors computer.

[0028]Other parameters, referred to herein as active parameters, are
available at some websites, for example, demographics information at
financial websites or transaction information available at e-commerce
websites, are more personal, but may be obtained with corresponding
passive parameters without compromising a web site visitor's privacy. The
active parameters are generally directly related to behaviors or other
attributes which may be estimated using mappings from the passive
parameters.

[0029]In the first phase, passive parameters 12a and corresponding
demographics 11 are received by a financial website 14 from first website
visitors 10a. The financial information includes the financial and other
data used by the financial website 14 in its normal course of operation.
Such information may include age, gender, education, address, income,
etc. of each website visitor 10a. The use of this data by the present
invention does not require identifying individual website visitors,
specifically, privacy is maintained. The combined passive parameters and
demographics data 16 is processed by a statistical mapping generator 18
(described in more detail below) to generate the statistical mappings 20
from the passive parameters to visitor demographics.

[0030]Continuing with FIG. 1, in the second phase, the statistical
mappings 20 are applied by a mapper 24 to individual second visitor
passive parameters 12b (which do not include personal or financial
information) received by an e-commerce website 22 from second website
visitors 10b to generate estimated demographics 26. The individual
demographics 26 and advertising demographic targets 30a from advertisers
32 are compared in advertisement selection 28a to select Internet
advertising 34 provided to the website visitor 10b. The selection of
advertising may include both the objects advertised and the manner of
presenting the advertisements to the website visitor 10b. The selection
may also be the result of bidding by the advertisers 32, where the bids
are to some extent based on the estimated demographics 26. Further, the
demographics mappings 20 may be provided to the advertisers 32, and the
advertisers 32 may form advertising targeting strategy and negotiate with
the e-commerce website 22 on advertising rates based on the visitor
parameters 12b. Several advertisers 32 may provide overlapping targets
30a, and the advertisement selection 28a may select to provide
advertising 34 having the highest profit, increased user registration, or
any result providing value to the advertiser or web site operator.

[0031]A diagram of a second embodiment of generation and application of a
mapping between passive website visitor parameters and active parameters
according to the present invention is shown in FIG. 2. The second
embodiment provides the same passive parameters 12a from first website
visitors 10a to an e-commerce website 22a, but does not rely on
demographic information provided by the website visitors 10a. The active
parameters are transaction data from actual transactions on the
e-commerce website 22a. The combined passive parameters and transaction
data 16 is provided to the statistical mapping generator 16 to generate
statistical mappings 20.

[0032]In the second phase, the statistical mappings 20 are provided to the
advertisers 32, and the advertisers 32 provide second targets 30b which
describe the passive visitor parameters 12b the advertisers desire to
target advertising to. The e-commerce website 22b then compares the
passive visitor parameters 12b to the targets 30b in a second
advertisement selection 28b to determine advertising 34 provided to the
website visitor 10b. Several advertisers 32 may provide overlapping
targets 30b, and the advertisement selection 28b may select to provide
advertising 34 having the greatest value.

[0033]A method for selecting website content according to the present
invention is described in FIG. 3. The method for selecting website
content includes obtaining a multiplicity of anonymous first website
visitor passive parameters from a first website for a multiplicity of
first website visitors visiting the first website, where the anonymous
first website visitor passive parameters are commonly available whenever
a website is visited at step 100, obtaining corresponding first website
visitor active parameters of the first website visitors from the first
website, where the website visitor active parameters are correlatable to
a value of website content presentable to future website visitors, at
step 102, generating anonymous statistical mappings between the first
website visitor passive parameters of the first website visitors and the
corresponding first website visitor active parameters of the first
website visitors, where the statistical mappings do not rely on any data
identifying an individual website visitor, at step 104, marketing the
statistical mappings to one of website operators, website publishers,
and/or Internet advertisers at step 106, using the statistical mappings
to establish an expected value of future website content provided to the
future website visitors, based on future website visitor passive
parameters of each of the future website visitors, at step 108, and
providing future website content to the future website visitors based on
the expected value of the future website content at step 110.

[0034]A method for budgeting advertising to visitors based on the expected
values of the advertising according to the present invention is described
in FIG. 4. The method for budgeting advertising to visitors based on the
expected values of the advertising includes obtaining a multiplicity of
anonymous first website visitor passive parameters from a financial
website for a multiplicity of financial website visitors visiting the
financial website, where the anonymous first website visitor passive
parameters are commonly available whenever a website is visited, at step
200, obtaining corresponding financial website visitor demographics of
the financial website visitors, where a value of website content
presentable to future website visitors is correlatable to the financial
website visitor demographics, at step 202, anonymously generating
statistical mappings between the first website visitor passive parameters
and the financial website visitor demographics of the financial website
visitors, where the statistical mappings do not rely on any data
identifying an individual website visitor, at step 204, marketing the
statistical mappings to at least one of website publishers, Internet
advertisers, and/or second website operators at step 206, using the
statistical mappings to either identify future website visitors having
demographics desirable to the website publishers and Internet advertisers
at step 208, and/or demonstrating demographics of the second website
visitors visiting second websites by the second website operators to
attract website publishers and/or Internet advertisers at step 210.

[0035]A second method for marketing Internet advertising according to the
present invention is described in FIG. 5. The method for marketing
Internet advertising includes obtaining anonymous first website visitor
passive parameters from an e-commerce website for a multiplicity of
e-commerce website visitors visiting the e-commerce website, where the
anonymous first website visitor passive parameters are commonly available
whenever a website is visited, at step 300, obtaining transaction data
from the e-commerce website corresponding to the first website visitor
passive parameters for the e-commerce website visitors at step 302,
generating statistical mappings from the first website visitor passive
parameters to the transaction data, where the statistical mappings do not
rely on any data identifying an individual website visitor, at step 304,
marketing the statistical mappings to website publishers and Internet
advertisers at step 306, estimating expected values of Internet
advertising to future website visitors based on future website visitor
passive parameters of the future website visitors and the statistical
mappings at step 308, and allocating the Internet advertising to the
future website visitors based on the expected values of the advertising
at step 310.

[0036]The method for marketing Internet advertising may further include
improving the statistical mapping as shown in FIG. 6. The additional
steps include collecting additional transaction data for the Internet
advertising directed to the future website visitors at step 312 and
generating improved statistical mappings based on the additional
transaction data at step 314.

[0037]As an example of the application of the present invention, an
Internet Search Engine (ISE) sends a large number of search engine
visitors to an e-commerce website. The e-commerce website compares the
value of the ISE visitors to the e-commerce website to the visitor's
passive parameters to obtain a statistical mapping. The e-commerce
website shares the statistical mapping with the ISE. The ISE then is more
aggressive in displaying the e-commerce website ads to ISE visitors of
high value to the e-commerce website and is less aggressive, or stops
entirely, displaying ads to search engine visitors of low value to the
e-commerce website. The ISE and the e-commerce website also adjust the
pricing of clicks and referrals based on the available parameters.

[0038]Several methods for generating the statistical mappings are
available. A direct mapping method uses a regression or other statistical
techniques to estimate the likelihood that the website visitor has a
specific characteristic, based on their browser agent. First, a large
database of records which include website visitor active parameters
(e.g., financial or personal information) from a website and
corresponding website visitor passive parameter (e.g., browser agents) is
collected, and a regression or other statistical techniques is used to
estimate the likelihood that the website visitor has a specific
demographic characteristic, based on their browser agent. The financial
and personal information and matching browser agent may be collected
from, for example, demographic information collected by a credit bureau.
Some websites have usable logs which may be correlated to the financial
and personal information. For example, virtually all web logs track
browser agent and may also note the username. A record of the web browser
agent and the specific website information (in the example, the consumer
demographic information, but not their name, Social security number, nor
other unique identifiers) is collected. When a large number of records
have been collected, statistical techniques are employed, to match the
browser agent to a demographic profile. For each user agent, a profile
(for example, 60% male, 40% female; 20% over 50 years old, 30% are 30-49
years old, 30% are 21-29 years old, 20% under 21 years old; 40% over $100
k income, etc.) is developed. Although a single visit at a website by a
browser does not yield statistically useful information, a sufficient
number of visits creates a statistically significant demographic profile.

[0039]Another method for generating statistical mappings is Multi-Factor
Mapping. In some instances, a website, might want to change its offering
to a visitor based on combinations of several factors. For example, a web
retailer may use web browser agents to define customer segments and
tailor the advertising to the browser agent. As in the Direct Mapping
example above, statistically meaningful visitor information required to
generate mappings from the browser agent to visitor characteristics is
obtained. A retailer may desire to split their visitors into four
segments: information hungry consumers that want lots of data before they
buy; men that are active purchasers on the web; women that are active
purchasers on the web; and others. Visitor browser information may be
collected from an advice site (for example, CNET) and define certain
browser agents which correlate with visitors seeking an information
intensive web experience. For visitors not in the information intensive
segment, information on demographics (e.g., from a credit bureau) and on
tendency to purchase online (e.g., from e-commerce websites) may be
combined to identify browser agents which correlate with gender, incomes,
or other demographic data. Browser agents which do not correlate with any
of the above groups may be correlated with, for example, branding or
other useful discriminates.

[0040]Further, the mappings may be continuously updated. Preferably, an
advertiser works with a search engine or publisher to increase the value
of their business relationship by continuously updating mappings specific
to the advertiser/publisher combination. The advertiser may monitor its
own transactions and places a value on new transactions for each browser
agent. The advertiser may place clickable ads on the publishers website.
The publisher only displays the ads to consumers with browser agents
selected by the publisher. The advertiser may also pay differing rates
for clicks based on the browser agent. As visitors click-through to the
advertisers site, the advertiser adjusts the value of a single visitor
with each browser agent. Depending on the contractual relationship, the
advertiser either passes this information back to the publisher or simply
adjusts the rates the advertiser offers to pay for traffic based on the
browser agent and the source (i.e., the publisher). Over time, the
advertiser may assign different values for a particular browser agent for
each publisher. In other words, each combination of a browser agent and
publisher will have an estimated value (e.g., gross profit.)

[0041]There may be some browser agents which are very uncommon or are
uncommon on a specific site. As a result, it may be difficult to assign,
with any statistical confidence, any characteristics (e.g., demographics)
to the browser agent. As an alternative to the Direct Mapping described
above, all of the data collected, including data from other websites, may
be used to find a more common browser agent that has similar
characteristics to the uncommon agent. For example, in order to estimate
demographic data for a website, rather than ignore an uncommon browser
agent, use statistical techniques to assign characteristics to the
browser agent. It may be determined that on other sites, a complicated
and unusual browser agent, such as: Mozilla/4.0 (compatible; MSIE 7.0;
Windows NT 5.1; InfoPath.1)libwww-perl/5.808 has similar characteristics
to a simpler agent, such as: AMozilla/4.0 (compatible; MSIE 7.0; Windows
NT 5.1). The latter browser agents demographics may then be assigned to
the unusual browser agent.

[0042]In some instances, the browser agent will not be able to distinguish
unusual results. For example, if a website has nearly 100% men (for
example, Slashdot), almost all browser agents will map into demographic
profiles which, using direct mapping, will show a more balanced
percentage of men. This problem may be addressed by doing a second-order
analysis of the browser agents. An agreement may be made with a website
assumed to have an unusual demographic profile (for example, almost all
men, women, young people, etc.). Their data may be collected and mappings
generated using methods similar to the direct mapping example. A profile
of which browser agents are present may be generated and which are absent
in websites with extreme profiles. Statistical techniques may then be
employed to match the distribution of browser agents to an extreme
profile. In addition to the results of the direct mapping, the estimates
may be adjusted based on the distribution of browser agents.

[0043]While the invention herein disclosed has been described by means of
specific embodiments and applications thereof, numerous modifications and
variations could be made thereto by those skilled in the art without
departing from the scope of the invention set forth in the claims.